Groundwater, the water in aquifers accessible by wells, is a critical component of the U.S. water supply. It is important for both domestic and agricultural water needs, among other uses. Nearly half of the nation’s population uses groundwater to meet daily needs; in 2015, about 149 million people (46% of the nation’s population) relied on groundwater for their domestic indoor and outdoor water supply.
For some, this resource is readily available and in great supply. In the agriculture industry, however, that is not always the case. Access and proper use of water is imperative for the sustainability, growth and livelihood of agriculture.
Consistently stressed conditions can have dramatic long-term effects on groundwater. If pumping continues in excess of recharge, increasing stress on the aquifer, the water table may drop tens to hundreds of feet. This situation has occurred in many regions of the United States. Underlying the Great Plains in eight states, the Ogallala aquifer supports nearly one-fifth of the wheat, corn, cotton and cattle produced in the USA. It has long been the main water supply for the High Plains’ population and is being used at an unsustainable rate.
Old MacDonald Had A Farm system integrates disparate systems to collect data, make observations and assessments, and provide information on groundwater supplies that supports the farmers as the decision-makers.
Off-the-shelf smart watering kits with self contained system on chips (SOC) modules with integrated TCP/IP protocol stack that can give the Arduino microcontrollers access to the WiFi network can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analyzed to filter out invalid data and compute tailored crop recommendations for any specific farm.
For this project, the team presents an IoT-based platform created during a weekend that can automate the collection of environmental, soil, fertilization, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalized crop recommendations for any particular farm. The focus is to demonstrate the possibility of the integrating virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations.